The present invention relates to personalization of computational models of human organs, such as the heart, and more particularly, to estimation of patient-specific model parameters and their uncertainty given noise in the data, model assumptions and estimation limitations.
Computational models have been recently explored for various human organs. For example, a motivation for the use of cardiac modeling relates to management of cardiomyopathy, one of the most common types of cardiovascular disease with significant mortality and morbidity rates. Cardiomyopathy is among the main causes of heart failure in developed countries. Clinical management of cardiomyopathy patients is challenging due to the wide variety of disease etiology and therapy options. Computational heart models are being explored as tools to improve patient stratification, risk prediction, therapy planning, guidance and follow-up. However, the high level of model complexity, their unavoidable simplifying assumptions and the limited availability of often noisy measurements hinder the personalization of these models that is necessary for such computational heart models to be clinically useful.